Performance prediction plays an important role in the management of large-scale waterfloods and miscible gasfloods. Computer simulation methods/tools are used to predict the ultimate recovery of hydrocarbons in the design of a flooding process and optimization of flooding operations. Typically, a large number of injectors and producers are aligned in patterns in a patterned flood. The operation of such a patterned flood can be complex, because fluid flows from injectors to producers in the reservoir are complex and interacting. Displacement performance within a given pattern is often influenced by the injection activities and production activities of surrounding flood patterns. To achieve optimal oil production, some wells may be injecting or producing at their maximum rates. Yet other wells may be shut in or choked back in a given period, and the flow rates into injectors and from producers may change from time to time. In addition, for EOR processes such as miscible gasfloods the allocation of expensive miscible solvent among injectors is not straightforward.
It is highly desirable to assess quickly and accurately the flooding performance of a hydrocarbon-bearing reservoir during the operation of large-scale, pattern waterfloods or enhanced oil recovery (EOR) projects such as miscible gasfloods. Many different techniques have been proposed for predicting performance of pattern floods. Here, the four most common methods are referred to as:                3-D, 3-phase finite-difference simulation,        3-D, 3-phase streamline simulation,        Conventional production type-curve scale-up methods,        Streamline-based forecasting methods.These four methods are briefly described below.        
3-D, 3-phase finite-difference simulation: The 3-D, 3-phase finite-difference reservoir simulation method models the full physics (e.g. reservoir geology, multi-phase flow and phase interactions) of multi-phase flow in a hydrocarbon-bearing reservoir that is represented by a 3-D geological model. The principle is to solve equations describing physical phenomena by a computer. The reservoir system is divided into small gridcells or blocks with each characterized by sets of variables such as porosity and permeability. The physical displacement system is described by a set of algebraic equations to express the fundamental principles of conservation of mass, energy, and momentum within each gridcell and transfer of mass, energy, and momentum between gridcells. This time-dependent transport of gas, oil, and water phases is modeled in a sequence of timesteps. The computation is especially complicated for EOR processes such as miscible gas flooding that requires multiple components to characterize the oil and gas phases.
Finite-difference simulation of pattern waterfloods and EOR processes typically require a large number of gridcells and small timesteps. A large reservoir with hundreds of thousands of grid blocks, hundreds of wells, and an extensive production history requires substantial computing resources. A single field simulation run can take several days to several weeks to complete. For large pattern floods, finite-difference simulation is often conducted using small models representing only a segment of the field or an element of a typical pattern. The element-model simulation is used to study flood mechanisms and generate production type-curves for full-field performance prediction. Full-field, 3-D, 3-phase, finite difference simulation can be used for detailed reservoir studies. Due to its lengthy computation, it is often not used for making operating decisions in day-to-day management of large pattern floods, especially for EOR processes.
3-D, 3-phase streamline simulation: The 3-D, 3-phase streamline simulation method models most of the flow physics in a 3-D geological model. It determines 3-D, single-phase flow streamlines, followed by calculating multi-phase fluid saturations along these streamlines. The underlying principle is to decouple the governing equations of fluid motion from a 3-D problem to multiple 1-D problems solved along streamlines. Streamline simulation can be used to simulate the performance of large pattern waterfloods and miscible gasfloods. In modeling large-scale waterflooding operations, it can be several times faster than 3-D, 3-phase finite-difference simulation. Examples of this method are disclosed in (a) Grinestaff, G. H. and Caffrey, Daniel J., Waterflood Management: A Case Study of the Northwest Fault Block Area of Prudhoe Bay, Ak., Using Streamline Simulation and Traditional Waterflood Analysis, SPE 63152, presented at the 2000 SPE Annual Technical Conference and Exhibition held in Dallas, Tex., October 2000 and (b) Lolomari, T., Bratvedt, K., Crane, M., Milliken, W. J., Tyrie, J. J., The Use of Streamline Simulation in Reservoir Management: Methodology and Case Studies SPE 63157, presented at the 2000 SPE Annual Technical Conference and Exhibition held in Dallas, Tex., October 2000.
3-D, 3-phase streamline simulation of miscible gas flooding is significantly slower than that of waterflooding, because multiple components are involved in developing miscibility between the oil and injected miscible solvent. The slower computation limits the application of compositional streamline simulation for managing large-scale pattern miscible gasfloods.
Conventional type-curve scale-up: Conventional production type-curve scale-up methods are simple, quick, and rough. They typically involve the following steps. (1) Divide the reservoir into geometric injector-producer polygons, each confining fluid flow between an injector-producer pair. (2) Allocate injection throughput for each polygon using a geometric technique. (3) Calculate oil, gas, and water production from each polygon using the injection throughput and production type curves. (4) Sum production from the polygons connecting to each producer to obtain production rates from individual producers.
Conventional production type-curve scale-up is the most frequently used approach to predict flood performance for large pattern floods. However, it is oversimplified due to the use of geometric injector-producer polygons and geometric fluid allocations. Consequently, its predictive capability is highly limited.
Various ways have been proposed for choosing geometric injector-producer polygons; various dimensionless correlation equations have been proposed for expressing production type-curves. One example was given in a paper by Shaw, E. C., “A Simple Technique to Forecast CO2 Flood Performance,” SPE 23975, presented at the 1992 SPE Permian Basin Oil and Gas Recovery Conference held in Midland, Tex., March 1992. The Shaw method expresses production type-curves in simple correlation equations. Additionally, it uses geometric patterns, instead of geometric injector-producer polygons, as flow units and scales up one set of production type-curves to the entire field. Due to the limited fluid and reservoir properties built into the method, most of the model properties, such as fluid allocation, pattern pore volume, oil saturation, and reservoir heterogeneity, become tuning parameters in matching predictions with actual production.
A more complicated scale-up method was proposed for forecasting performance of a combined waterflood and miscible gasflood (see Wingard, J. S. and Redman, R. S., A Full-Field Forecasting Tool for the Combined Water/Miscible Gas Flood at Prudhoe Bay, SPE 28632, presented at the SPE 69th Annual Technical Conference and Exhibition held in New Orleans, La., September 1994). The Wingard et al. method uses a separate streamline program as a guide in choosing the geometric injector-producer polygons and associated fluid allocations. While this method is more accurate than those based strictly on geometric fluid allocation, it cannot respond to dynamic changes of well locations and rates. The Wingard et al. method expresses oil production type-curves in correlation equations for recovery due to a base waterflood and enhanced recovery due to miscible solvent injection.
The conventional production type-curve scale-up methods of the prior art provide less than desirable results for two reasons. First, the use of geometric polygons results in erroneous fluid allocations and poor predictive capabilities. Second, the use of fixed geometric polygons do not handle dynamic changes to well rates and locations.
Streamline-based forecasting: Streamline-based forecasting methods allocate fluids based on flow streamlines and use production type-curves formulated in various forms. Such methods were designed for better accuracy than the conventional production type-curve scale-up methods and for faster computation speed than the finite-difference and streamline simulation methods. Examples are disclosed in the following papers: (1) Emanuel, A. S., Alameda, G. K., Bohrens, R. A., Hewett, T. A., Reservoir Performance Prediction Methods Based on Fractal Geostatistics, SPE Reservoir Engineering, 311, August (1989) and (2) Giordano, R. M., Redman, R. S., Bratvedt, F., A New Approach to Forecasting Miscible WAG Performance at the Field Scale, SPE 36712, presented at the 1996 SPE Annual Technical Conference and Exhibition held in Denver, Colo., October 1996.
The Emanuel method generates field-wide 2-D stream-tubes and uses production type-curves in the form of oil, gas, and water fractional flows for each stream tube. Typical fractional flows are generated from finite-difference, 3-phase, 2-D cross-sectional simulation. These fraction flow curves account for displacement efficiency and vertical reservoir heterogeneity, while field-wide 2D stream-tubes accounted for areal reservoir heterogeneity. The Emanuel method provides more accurate fluid allocation than the conventional scale-up methods. However, it cannot handle changes to well rates, locations, and key process parameters such as solvent bank size.
The Giordano et al. method uses streamlines and tracer adsorption/desorption for forecasting the field-wide performance of miscible gasfloods. The performance of a miscible flood is divided into base waterflooding and enhanced oil recovery due to miscible gas injection. The performance of the base waterflood is calculated by 3-D, 3-phase streamline simulation as discussed above, while the performance of the enhanced recovery is calculated by a tracer adsorption-desorption scheme. The scheme involves generation of 2-D streamlines followed by estimation of fluid saturation along streamlines via prototype production that is formulated in tracer adsorption-desorption equations, coefficients, and inaccessible pore volumes. The tracer adsorption-desorption coefficients and inaccessible pore volumes are extracted by fitting the tracer adsorption-desorption equations with prototype production obtained from 3-D, 3-phase, element-model, finite-difference simulation. This method provides good fluid allocations and handles well changes. However, the expression of prototype production through the tracer adsorption and desorption formulation limits the flexibility of handling key process parameters, such as WAG, VGR and well flow rates.
A need exists for a computationally fast, sufficiently accurate, operationally flexible method for predicting the performance of large-scale pattern floods such as waterfloods and miscible gasfloods. The method should preferably handle changes to well flow rates, locations, and key process parameters such as WAG ratio, VGR and solvent bank size for field operations.